Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two distinct yet powerful paradigms fueling the next era of automation and intelligence. While one concentrates on content creation, the other focuses on goal-oriented, autonomous behavior.
This instructor-led training session, available either online or onsite, targets intermediate-level AI professionals and technical experts who aim to grasp how to develop, assess, and incorporate generative and agentic AI solutions into practical applications.
Upon completion of this training, participants will be equipped to:
- Grasp the architecture and capabilities of generative AI systems.
- Investigate the emergence of autonomous AI agents and their role in extending the functionality of Large Language Models (LLMs).
- Apply prompt engineering techniques and tool integrations for real-world deployments.
- Evaluate various models, tools, and methodologies to ensure responsible deployment.
Course Format
- Engaging lectures paired with interactive discussions.
- Practical application of generative and agentic AI tools in realistic scenarios.
- Directed exercises centered on content creation and autonomous workflow design.
Customization Options for the Course
- For those interested in a tailored training experience for this course, please reach out to us to arrange details.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Key differences and synergies between the two
- Industry use cases and current trends
Generative AI Architecture and Tools
- Transformer-based models: GPT, LLaMA, Claude, and others
- Comparing fine-tuning with in-context learning
- Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarization, and more
- Techniques such as few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- Definition and evolution of agentic AI
- Core architectures: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Establishing goals and decomposing tasks
- Integrating tools and APIs (search, memory, code execution)
- Coordinating multi-agent systems and incorporating human-in-the-loop supervision
Use Cases and Implementation Scenarios
- Distinguishing between content generation and task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning principles
- Experience using APIs or scripting languages such as Python
- Familiarity with prompt engineering or the utilization of large language models
Target Audience
- AI developers and engineers
- Innovation and Research & Development (R&D) teams
- Technical product managers interested in exploring agentic AI systems
Open Training Courses require 5+ participants.
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